fitModel {ccems} | R Documentation |
This function fits a model/hypothesis created by mkModel
.
It is typically passed to lapply
or clusterApplyLB
to
fit a list of model objects, typically within ems
.
fitModel(model)
model |
The output list of mkModel . |
The main output of this function is the report
component of its value (see below) which is also echoed to the screen during
computations.
The input argument model
extended to include the following fields:
echk |
A matrix that checks the TCC solver and model$fback . Matrix column names that end in Q should match their sans-Q counterparts. |
eSS |
The expected steady state concentrations of complexes and free reactants.
For each row of the data dataframe there is a row in this
matrix. Its contents are the TCC solver's expected free reactant concentrations
and the result of applying
model$fback to them to create expected complex concentrations. |
res |
The residuals of the fit. |
nData |
The number of data points/rows in the data dataframe model$d . |
SSE |
The initial and final sum of squared errors (i.e. residual sum of squares). |
AIC |
The initial and final Akaike Information Criterion values, corrected for small samples. Since nonlinear least squares is used
AIC = N*log(SSE/N)+2*P + 2*P*(P+1)/(N-P-1) + N*log(2*pi) + N where N = nData and P is the
number of estimated parameters (including the variance). |
nOptParams |
The number of optimized parameters, i.e. the length of the parameter vector sent to optim . |
hess |
This is TRUE if the determinant of the Hessian of the log-likelihood evaluated at the optimum is greater than zero,
i.e. if the hessian can be inverted to create a parameter estimate covariance matrix. |
CI |
Confidence intervals. Unlike those in model$report these are numeric rather than strings and these are
not expressed as concentrations raised to integer powers (in cases of complete dissociation constants). |
cpu |
The amount of computing time (in minutes) taken to fit the model. |
report |
An extension of model$params to include parameter point estimates and
confidence intervals (see CI above). The final
column holds numerics and the pointEstimate column holds strings of the
same numbers expressed as powers in cases
of complete dissociation constants. |
This work was supported by the National Cancer Institute (K25CA104791).
Tom Radivoyevitch (txr24@case.edu)
Radivoyevitch, T. (2008) Equilibrium model selection: dTTP induced R1 dimerization. BMC Systems Biology 2, 15.
library(ccems) topology <- list( heads=c("R1t0","R2t0"), sites=list( s=list( # s-site thread # m=c("R1t1"), # monomer 1 d=c("R2t1","R2t2") # dimer 2 ) ) ) g <- mkg(topology,TCC=TRUE) data(RNR) d1 <- subset(RNR,(year==2001)&(fg==1)&(G==0)&(t>0),select=c(R,t,m,year)) d2 <- subset(RNR,year==2006,select=c(R,t,m,year)) dRt <- rbind(d1,d2) names(dRt)[1:2] <- paste(strsplit(g$id,split="")[[1]],"T",sep="")#e.g. to form "RT" rownames(dRt) <- 1:dim(dRt)[1] # lose big number row names of parent dataframe ## Not run: models <- list( mkModel(g,"IIJJ",dRt,Kjparams=c(R2t0=Inf, R1t1=Inf,R2t1=1, R2t2=1)), mkModel(g,"IIIJ",dRt,Kjparams=c(R2t0=Inf, R1t1=Inf,R2t1=Inf, R2t2=1)) ) # the next line fits the list of two models above in series on a single processor fmodels <- lapply(models,fitModel) ## End(Not run) # Note that fitModel always delivers a summary of the fit to the screen as a byproduct. # The output of the call is assigned to avoid scrolling up through the returned large # fitted list of models in order to find this summary.